You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Denoising autoencoders

Autoencoders can be used to determine under-complete representations of a dataset. However, Bengio et al. (in Vincent P., Larochelle H., Lajoie I., Bengio Y., Manzagol P., Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, from the Journal of Machine Learning Research, 11/2010) proposed using autoencoders to denoise the input samples rather than learning the exact representation of a sample in order to rebuild it from low-dimensional code.

This is not a brand-new idea, because, for example, Hopfield networks (proposed a few decades ago) had the same purpose, but their limitations in terms of capacity led researchers to look for different methods. Nowadays, deep autoencoders can easily manage high-dimensional data (such as images) with a consequent space requirement. That's why many people are now reconsidering the idea of teaching a network how to rebuild a sample image starting from...